{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data-driven modeling of a Resistance-Capacitance network with Neural ODEs\n",
    "\n",
    "This tutorial performs the data-driven modeling of a network of capacitive agents that are coupled by resistive connections.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Energy balance in dissipative systems**\n",
    "\n",
    "In many physical systems, behaviors over time are driven by input-output energy balances as prescribed by the [First Law of Thermodynamics](https://en.wikipedia.org/wiki/First_law_of_thermodynamics); i.e.:\n",
    "$$\n",
    "\\Delta U = Q - W,\n",
    "$$\n",
    "where $U$ is internal energy, $Q$ is heat added to the system, and $W$ is work done by the system on it surroundings. This energy accounting scheme can be extended to many situations. For example, suppose we wish to track the temperature ($T$) of a system with some thermal capacity ($C$) through time ($t$). After recasting internal energy as $U = C \\cdot T$, this yields a time-dependent form of the First Law of Thermodynamics:\n",
    "$$\n",
    "\\frac{dT}{dt} = \\frac{1}{C}\\left( \\dot{Q} - \\dot{W} \\right),\n",
    "$$\n",
    "with the heating and work terms now in units of power. This provides a compact framework for analysis of the time dynamics of systems that interact with their surroundings through heating and/or mechanical work. \n",
    "\n",
    "Here, we're concerned with systems comprised of many interacting *capacitive agents* - that is, a network of agents with a First Law of Thermodynamics input/output energy balance. This is the foundation for many engineering applications, such as building thermal dynamics. Each closed volume in a building has some thermal capacitance and is coupled via heat flows - through walls, heating, ventilation, air conditioning, etc. - with other capacitive agents. The couplings between capacitive agents are analogous to resistors in a circuit, acting to dissipate power across its connection points. An isolated capacitive agent subject to resistive energy transfer forms a [resistance-capacitance ciruit, or *RC Ciruit*](https://en.wikipedia.org/wiki/RC_circuit). Many coupled agents form a *RC Network*.\n",
    "\n",
    "A resistively-driven single zone RC agent obeys the First Law energy balance as:\n",
    "$$\n",
    "\\frac{dT}{dt} = \\frac{1}{C} \\left( \\frac{1}{R}\\left( T_{ext} - T \\right) \\right),\n",
    "$$\n",
    "where $T_{ext}$ is the forcing temperature. Note that no work is performed by the system on its surroundings. In the networked setting, the resistive couplings are summed over all of the connections for each agent in the network:\n",
    "$$\n",
    "\\frac{dT_i}{dt} = \\frac{1}{C_i} \\left( A_{i,j} \\sum_j^n  \\frac{1}{R_{i,j}} \\left( T_j - T_i \\right)  \\right),\n",
    "$$\n",
    "where $n$ is the number of agents in the network, $i,j \\in \\{1, 2, \\dots , n\\}$,  $A_{i,j}$ is the $(0,1)$-*adjacency matrix* defining the network structure (pairwise connections), and $R_{i,j}$ is the resistance for the pairwise interaction.\n",
    "\n",
    "### References\n",
    "[1] [Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud, Neural Ordinary Differential Equations, NeurIPS 2018](https://arxiv.org/abs/1806.07366)  \n",
    "[2] [Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, Ali Ramadhan, Alan Edelman, Universal Differential Equations for Scientific Machine Learning, arXiv:2001.04385](https://arxiv.org/abs/2001.04385)  \n",
    "[3] [L. Di Natale, B. Svetozarevic, P. Heer, C.N. Jones,\n",
    "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis,\n",
    "Applied Energy, Volume 325, 2022](https://www.sciencedirect.com/science/article/pii/S0306261922010819)  \n",
    "[4] [J. Koch, Z. Chen, A. Tuor, J. Drgona, D. Vrabie; Structural inference of networked dynamical systems with universal differential equations. Chaos 1 February 2023](https://pubs.aip.org/aip/cha/article-abstract/33/2/023103/2875955/Structural-inference-of-networked-dynamical?redirectedFrom=fulltext)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modeling with NeruoMANCER**\n",
    "\n",
    "Our task is to leverage the known structure of RC networks to estimate capacitances and coupling resistivities from data. We will use the NeruoMANCER package for this task; let's do our imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x18fec5d4bf0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy + plotting utilities + ordered dicts\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "\n",
    "\n",
    "# Standard PyTorch imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Neuromancer imports\n",
    "from neuromancer.psl.coupled_systems import *\n",
    "from neuromancer.dynamics import integrators, ode, physics, interpolation\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.system import Node, System\n",
    "from neuromancer.loggers import BasicLogger\n",
    "from neuromancer.trainer import Trainer\n",
    "\n",
    "# Fix seeds for reproducibility\n",
    "np.random.seed(200)\n",
    "torch.manual_seed(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Generation**\n",
    "\n",
    "Let's generate some data from a 5-zone RC network using NeuroMANCER's built-in RC-Network simulator. First we define a graph structure for our RC network as an array of pairwise connections and then hand this to PSL's RC Network class. A notional diagram of the network is shown below: 5 agents are coupled in a network, all five are connected to an outdoor temperature and a unique heating source."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![RC-Net diagram](../ODEs/figs/rc_net.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj = np.array([[0,1],[0,2],[0,3],[1,0],[1,3],[1,4],[2,0],[2,3],[3,0],[3,1],[3,2],[3,4],[4,1],[4,3]]).T\n",
    "s = RC_Network(nx=5, adj=adj)\n",
    "nsim = 500\n",
    "sim, s_dev, s_test = [s.simulate(nsim=nsim) for _ in range(3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can quickly take a look at the evolution of zonal temperatures over the length of the timeseries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18ff9c9ccd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Zone 1', 'Zone 2', 'Zone 3', 'Zone 4', 'Zone 5']\n",
    "plt.plot(sim['Time'],sim['X'], label=labels)\n",
    "plt.xlabel('$time$')\n",
    "plt.ylabel('Temperature (K)')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And also the external inputs to the system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18ff9e362b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Outdoor air temperature', 'Zone 1 discharge air temperature', 'Zone 2 discharge air temperature',\n",
    "           'Zone 3 discharge air temperature', 'Zone 4 discharge air temperature', 'Zone 5 discharge air temperature']\n",
    "plt.plot(sim['Time'],sim['U'], label=labels)\n",
    "plt.xlabel('$time$')\n",
    "plt.ylabel('Temperature (K)')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets are handled by PyTorch DataLoaders viw DictDatasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nstep = 10\n",
    "\n",
    "train_data = {'Y': sim['Y'], 'X': sim['X'], 'U': sim['U']}\n",
    "dev_data = {'Y': s_dev['Y'], 'X': s_dev['X'], 'U': s_dev['U']}\n",
    "test_data = {'Y': s_test['Y'], 'X': s_test['X'], 'U': s_test['U']}\n",
    "for d in [train_data, dev_data]:\n",
    "    d['X'] = d['X'].reshape(nsim//nstep, nstep, 5)\n",
    "    d['Y'] = d['Y'].reshape(nsim//nstep, nstep, 5)\n",
    "    d['U'] = d['U'].reshape(nsim//nstep, nstep, 6)\n",
    "    d['xn'] = d['X'][:, 0:1, :]\n",
    "\n",
    "train_dataset, dev_dataset, = [DictDataset(d, name=n) for d, n in zip([train_data, dev_data], ['train', 'dev'])]\n",
    "train_loader, dev_loader, test_loader = [DataLoader(d, batch_size=nsim//nstep, collate_fn=d.collate_fn, shuffle=True) for d in [train_dataset, dev_dataset, dev_dataset]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Construction\n",
    "\n",
    "Now that we have our data ready to go, let's construct our model for the RC Network system. We have many options for modeling such systems ranging from completely data-driven methods (like Dynamic Mode Decomposition), to data-driven equation-based methods (like Neural ODEs or Sparse Identification of Nonlinear Dynamics). Here, we wish to leverage the known structure of the system to construct a parameter-tuning problem in NeuroMANCER. To that end, we need to create our 5-zone ``building'' and all of the resistive connections that make up the network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Agents** \n",
    "\n",
    "At the core of a networked dynamical system, we define *agents* as objects possessing a dynamical state that evolves according to an Ordinary Differential Equation (ODE). These can be rooms in a building, a drone in a swarm, etc. In this case, we wish to create five capacitive agents to represent our building from our dataset. Here, we define the zones to be a list of 5 `RCNode`, each with a trainable parameter $C$ - the capacitance for the agent, and a scaling factor. This scaling factor is useful for including if you have prior knowledge of the approximate time constant for the agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "zones = [physics.RCNode(C=nn.Parameter(torch.tensor(5.0)),scaling=1.0e-5) for i in range(5)]  # heterogeneous population w/ identical physics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each zone in this network has its own heater. We include these heaters in the model via a `SourceSink` node - essentially a placeholder object for connecting our zones to external inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "heaters = [physics.SourceSink() for i in range(5)] # define heaters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, each zone is also in contact with the ambient environment (the outdoors). We represent outside as an additional `SourceSink` agent and then concatenate these lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "outside = [physics.SourceSink()]  \n",
    "\n",
    "# join lists:\n",
    "agents = zones + heaters + outside"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we connect our agents together in a graph, we need to define a mapping between our agents in the list and the indices of their respective states in the dataset. For this, we use a quick helper function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OrderedDict([('T', 0)]), OrderedDict([('T', 1)]), OrderedDict([('T', 2)]), OrderedDict([('T', 3)]), OrderedDict([('T', 4)]), OrderedDict([('T', 5)]), OrderedDict([('T', 6)]), OrderedDict([('T', 7)]), OrderedDict([('T', 8)]), OrderedDict([('T', 9)]), OrderedDict([('T', 10)])]\n"
     ]
    }
   ],
   "source": [
    "map = physics.map_from_agents(agents)\n",
    "# Let's take a look at this 'map':\n",
    "print(map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Couplings**\n",
    "\n",
    "From our instantiation of the PSL RC-Network, we already have an adjacency list corresponding to the connections among the 5 zones in our building. To avoid the tedium of individually constructing resistive connections between these agents, let's use a helper function to do it for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming new <class 'neuromancer.dynamics.physics.DeltaTemp'> for each element in edge list.\n",
      "14\n",
      "DeltaTemp()\n",
      "[array([0, 1])]\n"
     ]
    }
   ],
   "source": [
    "# Helper function for constructing couplings based on desired edge physics and an edge list:\n",
    "def generate_parameterized_edges(physics,edge_list):\n",
    "    \"\"\"\n",
    "    Quick helper function to construct edge physics/objects from adj. list:\n",
    "    \"\"\"\n",
    "\n",
    "    couplings = []\n",
    "    if isinstance(physics,nn.Module): # is \"physics\" an instance or a class?\n",
    "        # If we're in here, we expect one instance of \"physics\" for all edges in edge_list (homogeneous edges)\n",
    "        physics.pins = edge_list\n",
    "        couplings.append(physics)\n",
    "        print(f'Broadcasting {physics} to all elements in edge list.')\n",
    "    else: \n",
    "        # If we're in here, we expect different \"physics\" for each edge in edge_list (heterogeneous edges)\n",
    "        for edge in edge_list:\n",
    "            agent = physics(R=nn.Parameter(torch.tensor(50.0)),pins=[edge])\n",
    "            couplings.append(agent)\n",
    "\n",
    "        print(f'Assuming new {physics} for each element in edge list.')\n",
    "\n",
    "    return couplings\n",
    "\n",
    "couplings = generate_parameterized_edges(physics.DeltaTemp,list(adj.T))    # Heterogeneous edges of same physics\n",
    "\n",
    "# What do we have so far?\n",
    "print(len(couplings))\n",
    "# Let's take a look at one:\n",
    "print(couplings[0])\n",
    "# What's it connecting?\n",
    "print(couplings[0].pins)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to add connections between our `SourceSink` agents (the outdoors and the heaters) and the `RCNode` agents with more `DeltaTemp` resistive connections. These are easier to bookkeep, so let's do it manually. Here, the pins argument is expecting a list of arrays that specify pairwise interactions in the format (sender, receiver). Be sure that this order is consistent, especially when defining custom agents and couplings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Couple w/ outside temp:\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,5]]))\n",
    "\n",
    "# Couple w/ individual sources:\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,6]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,7]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,8]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,9]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,10]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to define our ODE System, integrator, and dynamics model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ode = ode.GeneralNetworkedODE(\n",
    "    map = map,\n",
    "    agents = agents,\n",
    "    couplings = couplings,\n",
    "    insize = s.nx+s.nu,\n",
    "    outsize = s.nx,\n",
    "    inductive_bias=\"compositional\")\n",
    "\n",
    "fx_int = integrators.RK2(model_ode, h=1.0)\n",
    "\n",
    "dynamics_model = System([Node(fx_int,['xn','U'],['xn'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loss and problem definitions**\n",
    "\n",
    "We define our loss to be a simple Mean-Squared Error between the reference trajectories ($X^{j}(t)$) and model trajectories ($\\hat{X}^{j}(t)$) evaluated at the collocation points $i$ for each $j$-batch:\n",
    "$$\n",
    "\\mathcal{L} = \\frac{1}{N_i \\cdot N_k} \\left( \\sum_i \\sum_j \\left( X^{j}(t_i) - \\hat{X}^{j}(t_i) \\right)^2\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Number of parameters: 29\n"
     ]
    }
   ],
   "source": [
    "x = variable(\"X\")\n",
    "xhat = variable(\"xn\")[:, :-1, :]\n",
    "\n",
    "reference_loss = ((xhat == x)^2)\n",
    "reference_loss.name = \"ref_loss\"\n",
    "\n",
    "objectives = [reference_loss]\n",
    "constraints = []\n",
    "\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "\n",
    "# construct constrained optimization problem\n",
    "problem = Problem([dynamics_model], loss)\n",
    "\n",
    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.01)\n",
    "logger = BasicLogger(args=None, savedir='test', verbosity=1,\n",
    "                     stdout=[\"dev_loss\",\"train_loss\"])\n",
    "\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_data,\n",
    "    optimizer,\n",
    "    epochs=1000,\n",
    "    patience=20,\n",
    "    warmup=5,\n",
    "    eval_metric=\"dev_loss\",\n",
    "    train_metric=\"train_loss\",\n",
    "    dev_metric=\"dev_loss\",\n",
    "    test_metric=\"dev_loss\",\n",
    "    logger=logger,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0\ttrain_loss: 0.01284\tdev_loss: 0.05561\teltime:  0.25205\n",
      "epoch: 1\ttrain_loss: 0.01278\tdev_loss: 0.05536\teltime:  0.57704\n",
      "epoch: 2\ttrain_loss: 0.01272\tdev_loss: 0.05510\teltime:  0.84812\n",
      "epoch: 3\ttrain_loss: 0.01266\tdev_loss: 0.05485\teltime:  1.12015\n",
      "epoch: 4\ttrain_loss: 0.01260\tdev_loss: 0.05460\teltime:  1.37500\n",
      "epoch: 5\ttrain_loss: 0.01254\tdev_loss: 0.05435\teltime:  1.67538\n",
      "epoch: 6\ttrain_loss: 0.01248\tdev_loss: 0.05410\teltime:  1.93220\n",
      "epoch: 7\ttrain_loss: 0.01242\tdev_loss: 0.05385\teltime:  2.20841\n",
      "epoch: 8\ttrain_loss: 0.01236\tdev_loss: 0.05360\teltime:  2.49175\n",
      "epoch: 9\ttrain_loss: 0.01231\tdev_loss: 0.05335\teltime:  2.87653\n",
      "epoch: 10\ttrain_loss: 0.01225\tdev_loss: 0.05310\teltime:  3.13448\n",
      "epoch: 11\ttrain_loss: 0.01219\tdev_loss: 0.05285\teltime:  3.37705\n",
      "epoch: 12\ttrain_loss: 0.01213\tdev_loss: 0.05260\teltime:  3.66636\n",
      "epoch: 13\ttrain_loss: 0.01207\tdev_loss: 0.05236\teltime:  3.92337\n",
      "epoch: 14\ttrain_loss: 0.01201\tdev_loss: 0.05211\teltime:  4.16764\n",
      "epoch: 15\ttrain_loss: 0.01196\tdev_loss: 0.05187\teltime:  4.45761\n",
      "epoch: 16\ttrain_loss: 0.01190\tdev_loss: 0.05163\teltime:  4.73752\n",
      "epoch: 17\ttrain_loss: 0.01184\tdev_loss: 0.05138\teltime:  5.01089\n",
      "epoch: 18\ttrain_loss: 0.01178\tdev_loss: 0.05114\teltime:  5.28018\n",
      "epoch: 19\ttrain_loss: 0.01173\tdev_loss: 0.05090\teltime:  5.53978\n",
      "epoch: 20\ttrain_loss: 0.01167\tdev_loss: 0.05066\teltime:  5.81230\n",
      "epoch: 21\ttrain_loss: 0.01161\tdev_loss: 0.05042\teltime:  6.09818\n",
      "epoch: 22\ttrain_loss: 0.01156\tdev_loss: 0.05018\teltime:  6.35454\n",
      "epoch: 23\ttrain_loss: 0.01150\tdev_loss: 0.04994\teltime:  6.62604\n",
      "epoch: 24\ttrain_loss: 0.01145\tdev_loss: 0.04970\teltime:  6.88568\n",
      "epoch: 25\ttrain_loss: 0.01139\tdev_loss: 0.04947\teltime:  7.14948\n",
      "epoch: 26\ttrain_loss: 0.01133\tdev_loss: 0.04923\teltime:  7.42847\n",
      "epoch: 27\ttrain_loss: 0.01128\tdev_loss: 0.04900\teltime:  7.73237\n",
      "epoch: 28\ttrain_loss: 0.01122\tdev_loss: 0.04876\teltime:  8.00197\n",
      "epoch: 29\ttrain_loss: 0.01117\tdev_loss: 0.04853\teltime:  8.24394\n",
      "epoch: 30\ttrain_loss: 0.01112\tdev_loss: 0.04830\teltime:  8.50300\n",
      "epoch: 31\ttrain_loss: 0.01106\tdev_loss: 0.04806\teltime:  8.76868\n",
      "epoch: 32\ttrain_loss: 0.01101\tdev_loss: 0.04783\teltime:  9.04321\n",
      "epoch: 33\ttrain_loss: 0.01095\tdev_loss: 0.04760\teltime:  9.35119\n",
      "epoch: 34\ttrain_loss: 0.01090\tdev_loss: 0.04737\teltime:  9.62027\n",
      "epoch: 35\ttrain_loss: 0.01085\tdev_loss: 0.04715\teltime:  9.88775\n",
      "epoch: 36\ttrain_loss: 0.01079\tdev_loss: 0.04692\teltime:  10.14805\n",
      "epoch: 37\ttrain_loss: 0.01074\tdev_loss: 0.04669\teltime:  10.43815\n",
      "epoch: 38\ttrain_loss: 0.01069\tdev_loss: 0.04647\teltime:  10.69076\n",
      "epoch: 39\ttrain_loss: 0.01063\tdev_loss: 0.04624\teltime:  10.93361\n",
      "epoch: 40\ttrain_loss: 0.01058\tdev_loss: 0.04602\teltime:  11.16591\n",
      "epoch: 41\ttrain_loss: 0.01053\tdev_loss: 0.04579\teltime:  11.42002\n",
      "epoch: 42\ttrain_loss: 0.01048\tdev_loss: 0.04557\teltime:  11.69303\n",
      "epoch: 43\ttrain_loss: 0.01042\tdev_loss: 0.04535\teltime:  11.93455\n",
      "epoch: 44\ttrain_loss: 0.01037\tdev_loss: 0.04513\teltime:  12.24176\n",
      "epoch: 45\ttrain_loss: 0.01032\tdev_loss: 0.04491\teltime:  12.51075\n",
      "epoch: 46\ttrain_loss: 0.01027\tdev_loss: 0.04469\teltime:  12.82642\n",
      "epoch: 47\ttrain_loss: 0.01022\tdev_loss: 0.04447\teltime:  13.08317\n",
      "epoch: 48\ttrain_loss: 0.01017\tdev_loss: 0.04425\teltime:  13.34523\n",
      "epoch: 49\ttrain_loss: 0.01012\tdev_loss: 0.04404\teltime:  13.59660\n",
      "epoch: 50\ttrain_loss: 0.01007\tdev_loss: 0.04382\teltime:  13.83879\n",
      "epoch: 51\ttrain_loss: 0.01002\tdev_loss: 0.04361\teltime:  14.11305\n",
      "epoch: 52\ttrain_loss: 0.00997\tdev_loss: 0.04339\teltime:  14.36309\n",
      "epoch: 53\ttrain_loss: 0.00992\tdev_loss: 0.04318\teltime:  14.61886\n",
      "epoch: 54\ttrain_loss: 0.00987\tdev_loss: 0.04297\teltime:  14.89964\n",
      "epoch: 55\ttrain_loss: 0.00982\tdev_loss: 0.04275\teltime:  15.18928\n",
      "epoch: 56\ttrain_loss: 0.00977\tdev_loss: 0.04254\teltime:  15.44572\n",
      "epoch: 57\ttrain_loss: 0.00972\tdev_loss: 0.04233\teltime:  15.74823\n",
      "epoch: 58\ttrain_loss: 0.00967\tdev_loss: 0.04213\teltime:  15.98673\n",
      "epoch: 59\ttrain_loss: 0.00962\tdev_loss: 0.04192\teltime:  16.24532\n",
      "epoch: 60\ttrain_loss: 0.00957\tdev_loss: 0.04171\teltime:  16.51833\n",
      "epoch: 61\ttrain_loss: 0.00953\tdev_loss: 0.04150\teltime:  16.80284\n",
      "epoch: 62\ttrain_loss: 0.00948\tdev_loss: 0.04130\teltime:  17.07744\n",
      "epoch: 63\ttrain_loss: 0.00943\tdev_loss: 0.04109\teltime:  17.36433\n",
      "epoch: 64\ttrain_loss: 0.00938\tdev_loss: 0.04089\teltime:  17.63546\n",
      "epoch: 65\ttrain_loss: 0.00933\tdev_loss: 0.04068\teltime:  17.92005\n",
      "epoch: 66\ttrain_loss: 0.00929\tdev_loss: 0.04048\teltime:  18.22104\n",
      "epoch: 67\ttrain_loss: 0.00924\tdev_loss: 0.04028\teltime:  18.52142\n",
      "epoch: 68\ttrain_loss: 0.00919\tdev_loss: 0.04008\teltime:  18.78927\n",
      "epoch: 69\ttrain_loss: 0.00915\tdev_loss: 0.03988\teltime:  19.10708\n",
      "epoch: 70\ttrain_loss: 0.00910\tdev_loss: 0.03968\teltime:  19.37584\n",
      "epoch: 71\ttrain_loss: 0.00905\tdev_loss: 0.03948\teltime:  19.66826\n",
      "epoch: 72\ttrain_loss: 0.00901\tdev_loss: 0.03928\teltime:  19.95548\n",
      "epoch: 73\ttrain_loss: 0.00896\tdev_loss: 0.03909\teltime:  20.20967\n",
      "epoch: 74\ttrain_loss: 0.00892\tdev_loss: 0.03889\teltime:  20.49080\n",
      "epoch: 75\ttrain_loss: 0.00887\tdev_loss: 0.03869\teltime:  20.78600\n",
      "epoch: 76\ttrain_loss: 0.00883\tdev_loss: 0.03850\teltime:  21.04587\n",
      "epoch: 77\ttrain_loss: 0.00878\tdev_loss: 0.03830\teltime:  21.35855\n",
      "epoch: 78\ttrain_loss: 0.00874\tdev_loss: 0.03811\teltime:  21.64326\n",
      "epoch: 79\ttrain_loss: 0.00869\tdev_loss: 0.03792\teltime:  21.89392\n",
      "epoch: 80\ttrain_loss: 0.00865\tdev_loss: 0.03773\teltime:  22.15631\n",
      "epoch: 81\ttrain_loss: 0.00860\tdev_loss: 0.03754\teltime:  22.44438\n",
      "epoch: 82\ttrain_loss: 0.00856\tdev_loss: 0.03735\teltime:  22.67984\n",
      "epoch: 83\ttrain_loss: 0.00851\tdev_loss: 0.03716\teltime:  22.98433\n",
      "epoch: 84\ttrain_loss: 0.00847\tdev_loss: 0.03697\teltime:  23.25885\n",
      "epoch: 85\ttrain_loss: 0.00843\tdev_loss: 0.03678\teltime:  23.51535\n",
      "epoch: 86\ttrain_loss: 0.00838\tdev_loss: 0.03660\teltime:  23.83182\n",
      "epoch: 87\ttrain_loss: 0.00834\tdev_loss: 0.03641\teltime:  24.11719\n",
      "epoch: 88\ttrain_loss: 0.00830\tdev_loss: 0.03622\teltime:  24.35816\n",
      "epoch: 89\ttrain_loss: 0.00825\tdev_loss: 0.03604\teltime:  24.66478\n",
      "epoch: 90\ttrain_loss: 0.00821\tdev_loss: 0.03586\teltime:  24.91851\n",
      "epoch: 91\ttrain_loss: 0.00817\tdev_loss: 0.03567\teltime:  25.17670\n",
      "epoch: 92\ttrain_loss: 0.00813\tdev_loss: 0.03549\teltime:  25.48013\n",
      "epoch: 93\ttrain_loss: 0.00809\tdev_loss: 0.03531\teltime:  25.75203\n",
      "epoch: 94\ttrain_loss: 0.00804\tdev_loss: 0.03513\teltime:  26.02120\n",
      "epoch: 95\ttrain_loss: 0.00800\tdev_loss: 0.03495\teltime:  26.26277\n",
      "epoch: 96\ttrain_loss: 0.00796\tdev_loss: 0.03477\teltime:  26.53731\n",
      "epoch: 97\ttrain_loss: 0.00792\tdev_loss: 0.03459\teltime:  26.80352\n",
      "epoch: 98\ttrain_loss: 0.00788\tdev_loss: 0.03441\teltime:  27.07217\n",
      "epoch: 99\ttrain_loss: 0.00784\tdev_loss: 0.03424\teltime:  27.33856\n",
      "epoch: 100\ttrain_loss: 0.00780\tdev_loss: 0.03406\teltime:  27.60444\n",
      "epoch: 101\ttrain_loss: 0.00775\tdev_loss: 0.03388\teltime:  27.86886\n",
      "epoch: 102\ttrain_loss: 0.00771\tdev_loss: 0.03371\teltime:  28.14076\n",
      "epoch: 103\ttrain_loss: 0.00767\tdev_loss: 0.03353\teltime:  28.44152\n",
      "epoch: 104\ttrain_loss: 0.00763\tdev_loss: 0.03336\teltime:  28.72652\n",
      "epoch: 105\ttrain_loss: 0.00759\tdev_loss: 0.03319\teltime:  28.95264\n",
      "epoch: 106\ttrain_loss: 0.00756\tdev_loss: 0.03302\teltime:  29.22797\n",
      "epoch: 107\ttrain_loss: 0.00752\tdev_loss: 0.03284\teltime:  29.51104\n",
      "epoch: 108\ttrain_loss: 0.00748\tdev_loss: 0.03267\teltime:  29.75755\n",
      "epoch: 109\ttrain_loss: 0.00744\tdev_loss: 0.03250\teltime:  30.00679\n",
      "epoch: 110\ttrain_loss: 0.00740\tdev_loss: 0.03234\teltime:  30.28352\n",
      "epoch: 111\ttrain_loss: 0.00736\tdev_loss: 0.03217\teltime:  30.59267\n",
      "epoch: 112\ttrain_loss: 0.00732\tdev_loss: 0.03200\teltime:  30.85618\n",
      "epoch: 113\ttrain_loss: 0.00728\tdev_loss: 0.03183\teltime:  31.11472\n",
      "epoch: 114\ttrain_loss: 0.00724\tdev_loss: 0.03167\teltime:  31.37679\n",
      "epoch: 115\ttrain_loss: 0.00720\tdev_loss: 0.03150\teltime:  31.62363\n",
      "epoch: 116\ttrain_loss: 0.00717\tdev_loss: 0.03133\teltime:  31.89021\n",
      "epoch: 117\ttrain_loss: 0.00713\tdev_loss: 0.03117\teltime:  32.16729\n",
      "epoch: 118\ttrain_loss: 0.00709\tdev_loss: 0.03101\teltime:  32.46698\n",
      "epoch: 119\ttrain_loss: 0.00705\tdev_loss: 0.03085\teltime:  32.71818\n",
      "epoch: 120\ttrain_loss: 0.00702\tdev_loss: 0.03068\teltime:  32.99366\n",
      "epoch: 121\ttrain_loss: 0.00698\tdev_loss: 0.03052\teltime:  33.25502\n",
      "epoch: 122\ttrain_loss: 0.00694\tdev_loss: 0.03036\teltime:  33.53812\n",
      "epoch: 123\ttrain_loss: 0.00691\tdev_loss: 0.03020\teltime:  33.81944\n",
      "epoch: 124\ttrain_loss: 0.00687\tdev_loss: 0.03004\teltime:  34.11512\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 125\ttrain_loss: 0.00683\tdev_loss: 0.02988\teltime:  34.37020\n",
      "epoch: 126\ttrain_loss: 0.00680\tdev_loss: 0.02972\teltime:  34.67126\n",
      "epoch: 127\ttrain_loss: 0.00676\tdev_loss: 0.02957\teltime:  34.97128\n",
      "epoch: 128\ttrain_loss: 0.00672\tdev_loss: 0.02941\teltime:  35.25646\n",
      "epoch: 129\ttrain_loss: 0.00669\tdev_loss: 0.02925\teltime:  35.55663\n",
      "epoch: 130\ttrain_loss: 0.00665\tdev_loss: 0.02910\teltime:  35.86611\n",
      "epoch: 131\ttrain_loss: 0.00662\tdev_loss: 0.02894\teltime:  36.15563\n",
      "epoch: 132\ttrain_loss: 0.00658\tdev_loss: 0.02879\teltime:  36.45009\n",
      "epoch: 133\ttrain_loss: 0.00655\tdev_loss: 0.02864\teltime:  36.73547\n",
      "epoch: 134\ttrain_loss: 0.00651\tdev_loss: 0.02848\teltime:  37.03905\n",
      "epoch: 135\ttrain_loss: 0.00648\tdev_loss: 0.02833\teltime:  37.29043\n",
      "epoch: 136\ttrain_loss: 0.00644\tdev_loss: 0.02818\teltime:  37.57691\n",
      "epoch: 137\ttrain_loss: 0.00641\tdev_loss: 0.02803\teltime:  37.82912\n",
      "epoch: 138\ttrain_loss: 0.00637\tdev_loss: 0.02788\teltime:  38.12300\n",
      "epoch: 139\ttrain_loss: 0.00634\tdev_loss: 0.02773\teltime:  38.41006\n",
      "epoch: 140\ttrain_loss: 0.00630\tdev_loss: 0.02758\teltime:  38.69509\n",
      "epoch: 141\ttrain_loss: 0.00627\tdev_loss: 0.02743\teltime:  38.96360\n",
      "epoch: 142\ttrain_loss: 0.00624\tdev_loss: 0.02729\teltime:  39.23777\n",
      "epoch: 143\ttrain_loss: 0.00620\tdev_loss: 0.02714\teltime:  39.50323\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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      "epoch: 919\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  253.57589\n",
      "epoch: 920\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  253.82339\n",
      "epoch: 921\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  254.08891\n",
      "epoch: 922\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  254.36709\n",
      "epoch: 923\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  254.62460\n",
      "epoch: 924\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  254.87234\n",
      "epoch: 925\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  255.11640\n",
      "epoch: 926\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  255.37858\n",
      "epoch: 927\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  255.62664\n",
      "epoch: 928\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  255.90377\n",
      "epoch: 929\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  256.14694\n",
      "epoch: 930\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  256.41634\n",
      "epoch: 931\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  256.69236\n",
      "epoch: 932\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  256.95940\n",
      "epoch: 933\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  257.23920\n",
      "epoch: 934\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  257.50669\n",
      "epoch: 935\ttrain_loss: 0.00009\tdev_loss: 0.00018\teltime:  257.75145\n",
      "epoch: 936\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  258.00630\n",
      "epoch: 937\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  258.28147\n",
      "epoch: 938\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  258.53492\n",
      "epoch: 939\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  258.77811\n",
      "epoch: 940\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  259.03206\n",
      "epoch: 941\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  259.29890\n",
      "epoch: 942\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  259.56382\n",
      "epoch: 943\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  259.81014\n",
      "epoch: 944\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  260.04305\n",
      "epoch: 945\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  260.30038\n",
      "epoch: 946\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  260.55571\n",
      "epoch: 947\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  260.81316\n",
      "epoch: 948\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  261.08389\n",
      "epoch: 949\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  261.33792\n",
      "epoch: 950\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  261.61021\n",
      "epoch: 951\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  261.88937\n",
      "epoch: 952\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  262.15877\n",
      "epoch: 953\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  262.40457\n",
      "epoch: 954\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  262.65960\n",
      "epoch: 955\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  262.90570\n",
      "epoch: 956\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  263.18603\n",
      "epoch: 957\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  263.45879\n",
      "epoch: 958\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  263.70546\n",
      "epoch: 959\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  263.98477\n",
      "epoch: 960\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  264.25210\n",
      "epoch: 961\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  264.51828\n",
      "epoch: 962\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  264.76347\n",
      "epoch: 963\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  265.04079\n",
      "epoch: 964\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  265.30870\n",
      "epoch: 965\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  265.56573\n",
      "epoch: 966\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  265.79816\n",
      "epoch: 967\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  266.02896\n",
      "epoch: 968\ttrain_loss: 0.00009\tdev_loss: 0.00017\teltime:  266.26537\n",
      "epoch: 969\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  266.51101\n",
      "epoch: 970\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  266.78468\n",
      "epoch: 971\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  267.04137\n",
      "epoch: 972\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  267.29710\n",
      "epoch: 973\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  267.56106\n",
      "epoch: 974\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  267.82660\n",
      "epoch: 975\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.08507\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 976\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.35431\n",
      "epoch: 977\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.63941\n",
      "epoch: 978\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.89738\n",
      "epoch: 979\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  269.17488\n",
      "epoch: 980\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  269.45078\n",
      "epoch: 981\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  269.70418\n",
      "epoch: 982\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  269.96026\n",
      "epoch: 983\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  270.21789\n",
      "epoch: 984\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  270.49751\n",
      "epoch: 985\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  270.73008\n",
      "epoch: 986\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.00640\n",
      "epoch: 987\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.26856\n",
      "epoch: 988\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.57694\n",
      "epoch: 989\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.84506\n",
      "epoch: 990\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.09982\n",
      "epoch: 991\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.36507\n",
      "epoch: 992\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.64258\n",
      "epoch: 993\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.90798\n",
      "epoch: 994\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.17319\n",
      "epoch: 995\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.43967\n",
      "epoch: 996\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.71024\n",
      "epoch: 997\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.97709\n",
      "epoch: 998\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  274.23435\n",
      "epoch: 999\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  274.49947\n"
     ]
    }
   ],
   "source": [
    "best_model = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**\n",
    "\n",
    "How did we do? Let's do a quick n-step rollout of the timeseries and compare with the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d6c4f37f0>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f3820>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f3850>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f0760>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f1960>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = torch.from_numpy(s_test['U']).float()\n",
    "sol = torch.zeros((500,5))\n",
    "ic = torch.tensor(s_test['X'][0,:5])\n",
    "for j in range(sol.shape[0]-1):\n",
    "    if j==0:\n",
    "        sol[[0],:] = ic.float()\n",
    "        sol[[j+1],:] = fx_int(sol[[j],:],u[[j],:])\n",
    "    else:\n",
    "        sol[[j+1],:] = fx_int(sol[[j],:],u[[j],:])\n",
    "\n",
    "plt.plot(sol.detach().numpy(),label='model', color = 'black')\n",
    "plt.plot(s_test['X'][:,:5],label = 'data', color = 'red')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
